{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"datasetVersion","sourceId":7016744,"datasetId":4034354,"databundleVersionId":7103901},{"sourceType":"datasetVersion","sourceId":6017451,"datasetId":3444172,"databundleVersionId":6095437},{"sourceType":"datasetVersion","sourceId":8760277,"datasetId":4299235,"databundleVersionId":8915569},{"sourceType":"datasetVersion","sourceId":5960502,"datasetId":3418322,"databundleVersionId":6038183}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install neurokit2","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-16T21:57:27.332325Z","iopub.execute_input":"2024-06-16T21:57:27.332679Z","iopub.status.idle":"2024-06-16T21:57:42.408465Z","shell.execute_reply.started":"2024-06-16T21:57:27.332650Z","shell.execute_reply":"2024-06-16T21:57:42.407127Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting neurokit2\n  Downloading neurokit2-0.2.9-py2.py3-none-any.whl.metadata (37 kB)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from neurokit2) (2.32.3)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from neurokit2) (1.26.4)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from neurokit2) (2.2.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from neurokit2) (1.11.4)\nRequirement already satisfied: scikit-learn>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from neurokit2) (1.2.2)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from neurokit2) (3.7.5)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->neurokit2) (1.4.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->neurokit2) (3.2.0)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (1.2.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (4.47.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (1.4.5)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (9.5.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (3.1.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->neurokit2) (2.9.0.post0)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->neurokit2) (2023.3.post1)\nRequirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas->neurokit2) (2023.4)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->neurokit2) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->neurokit2) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->neurokit2) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->neurokit2) (2024.2.2)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->neurokit2) (1.16.0)\nDownloading neurokit2-0.2.9-py2.py3-none-any.whl (689 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.2/689.2 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: neurokit2\nSuccessfully installed neurokit2-0.2.9\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install hrv-analysis\n!pip install pyhrv","metadata":{"execution":{"iopub.status.busy":"2024-06-16T21:57:42.410858Z","iopub.execute_input":"2024-06-16T21:57:42.411236Z","iopub.status.idle":"2024-06-16T21:58:13.890651Z","shell.execute_reply.started":"2024-06-16T21:57:42.411190Z","shell.execute_reply":"2024-06-16T21:58:13.889453Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting hrv-analysis\n  Downloading hrv_analysis-1.0.4-py3-none-any.whl.metadata (8.1 kB)\nRequirement already satisfied: numpy>=1.15.1 in /opt/conda/lib/python3.10/site-packages (from hrv-analysis) (1.26.4)\nRequirement already satisfied: astropy>=3.0.4 in /opt/conda/lib/python3.10/site-packages (from hrv-analysis) (6.1.0)\nCollecting nolds>=0.4.1 (from hrv-analysis)\n  Downloading nolds-0.5.2-py2.py3-none-any.whl.metadata (4.6 kB)\nRequirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from hrv-analysis) (1.11.4)\nRequirement already satisfied: pandas>=0.23.4 in /opt/conda/lib/python3.10/site-packages (from hrv-analysis) (2.2.2)\nRequirement already satisfied: matplotlib>=2.2.2 in /opt/conda/lib/python3.10/site-packages (from hrv-analysis) (3.7.5)\nRequirement already satisfied: pyerfa>=2.0.1.1 in /opt/conda/lib/python3.10/site-packages (from astropy>=3.0.4->hrv-analysis) (2.0.1.4)\nRequirement already satisfied: astropy-iers-data>=0.2024.4.29.0.28.48 in /opt/conda/lib/python3.10/site-packages (from astropy>=3.0.4->hrv-analysis) (0.2024.6.3.0.31.14)\nRequirement already satisfied: PyYAML>=3.13 in /opt/conda/lib/python3.10/site-packages (from astropy>=3.0.4->hrv-analysis) (6.0.1)\nRequirement already satisfied: packaging>=19.0 in /opt/conda/lib/python3.10/site-packages (from astropy>=3.0.4->hrv-analysis) (21.3)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (1.2.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (4.47.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (1.4.5)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (9.5.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (3.1.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.2.2->hrv-analysis) (2.9.0.post0)\nRequirement already satisfied: future in /opt/conda/lib/python3.10/site-packages (from nolds>=0.4.1->hrv-analysis) (1.0.0)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from nolds>=0.4.1->hrv-analysis) (69.0.3)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.23.4->hrv-analysis) (2023.3.post1)\nRequirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.23.4->hrv-analysis) (2023.4)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=2.2.2->hrv-analysis) (1.16.0)\nDownloading hrv_analysis-1.0.4-py3-none-any.whl (28 kB)\nDownloading nolds-0.5.2-py2.py3-none-any.whl (39 kB)\nInstalling collected packages: nolds, hrv-analysis\nSuccessfully installed hrv-analysis-1.0.4 nolds-0.5.2\nCollecting pyhrv\n  Downloading pyhrv-0.4.1-py3-none-any.whl.metadata (11 kB)\nCollecting biosppy (from pyhrv)\n  Downloading biosppy-2.2.2-py2.py3-none-any.whl.metadata (5.7 kB)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from pyhrv) (3.7.5)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from pyhrv) (1.26.4)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from pyhrv) (1.11.4)\nRequirement already satisfied: nolds in /opt/conda/lib/python3.10/site-packages (from pyhrv) (0.5.2)\nCollecting spectrum (from pyhrv)\n  Downloading spectrum-0.8.1.tar.gz (230 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m230.8/230.8 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hCollecting bidict (from biosppy->pyhrv)\n  Downloading bidict-0.23.1-py3-none-any.whl.metadata (8.7 kB)\nRequirement already satisfied: h5py in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (3.10.0)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (1.2.2)\nCollecting shortuuid (from biosppy->pyhrv)\n  Downloading shortuuid-1.0.13-py3-none-any.whl.metadata (5.8 kB)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (1.16.0)\nRequirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (1.4.2)\nRequirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (4.10.0.82)\nRequirement already satisfied: pywavelets in /opt/conda/lib/python3.10/site-packages (from biosppy->pyhrv) (1.5.0)\nCollecting mock (from biosppy->pyhrv)\n  Downloading mock-5.1.0-py3-none-any.whl.metadata (3.0 kB)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (1.2.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (4.47.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (1.4.5)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (9.5.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (3.1.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->pyhrv) (2.9.0.post0)\nRequirement already satisfied: future in /opt/conda/lib/python3.10/site-packages (from nolds->pyhrv) (1.0.0)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from nolds->pyhrv) (69.0.3)\nCollecting easydev (from spectrum->pyhrv)\n  Downloading easydev-0.13.2-py3-none-any.whl.metadata (3.5 kB)\nRequirement already satisfied: colorama<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from easydev->spectrum->pyhrv) (0.4.6)\nRequirement already satisfied: colorlog<7.0.0,>=6.8.2 in /opt/conda/lib/python3.10/site-packages (from easydev->spectrum->pyhrv) (6.8.2)\nRequirement already satisfied: line-profiler<5.0.0,>=4.1.2 in /opt/conda/lib/python3.10/site-packages (from easydev->spectrum->pyhrv) (4.1.3)\nCollecting pexpect<5.0.0,>=4.9.0 (from easydev->spectrum->pyhrv)\n  Downloading pexpect-4.9.0-py2.py3-none-any.whl.metadata (2.5 kB)\nCollecting platformdirs<5.0.0,>=4.2.0 (from easydev->spectrum->pyhrv)\n  Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->biosppy->pyhrv) (3.2.0)\nRequirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.10/site-packages (from pexpect<5.0.0,>=4.9.0->easydev->spectrum->pyhrv) (0.7.0)\nDownloading pyhrv-0.4.1-py3-none-any.whl (3.2 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m44.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading biosppy-2.2.2-py2.py3-none-any.whl (149 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.4/149.4 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading bidict-0.23.1-py3-none-any.whl (32 kB)\nDownloading easydev-0.13.2-py3-none-any.whl (56 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.8/56.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading mock-5.1.0-py3-none-any.whl (30 kB)\nDownloading shortuuid-1.0.13-py3-none-any.whl (10 kB)\nDownloading pexpect-4.9.0-py2.py3-none-any.whl (63 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.8/63.8 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading platformdirs-4.2.2-py3-none-any.whl (18 kB)\nBuilding wheels for collected packages: spectrum\n  Building wheel for spectrum (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for spectrum: filename=spectrum-0.8.1-cp310-cp310-linux_x86_64.whl size=227335 sha256=e6552c01eb9d4a62bf5463fe5b7794975b806fd419f5019c702a7fd7b5bea936\n  Stored in directory: /root/.cache/pip/wheels/e7/5a/09/ffc6afdf8a5a6f58e9851292108df32bb11374e11b8705cabd\nSuccessfully built spectrum\nInstalling collected packages: shortuuid, platformdirs, pexpect, mock, bidict, easydev, spectrum, biosppy, pyhrv\n  Attempting uninstall: platformdirs\n    Found existing installation: platformdirs 3.11.0\n    Uninstalling platformdirs-3.11.0:\n      Successfully uninstalled platformdirs-3.11.0\n  Attempting uninstall: pexpect\n    Found existing installation: pexpect 4.8.0\n    Uninstalling pexpect-4.8.0:\n      Successfully uninstalled pexpect-4.8.0\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nconda 24.5.0 requires packaging>=23.0, but you have packaging 21.3 which is incompatible.\njupyterlab 4.2.1 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\njupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\nlibpysal 4.9.2 requires packaging>=22, but you have packaging 21.3 which is incompatible.\nlibpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nmomepy 0.7.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nvirtualenv 20.21.0 requires platformdirs<4,>=2.4, but you have platformdirs 4.2.2 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed bidict-0.23.1 biosppy-2.2.2 easydev-0.13.2 mock-5.1.0 pexpect-4.9.0 platformdirs-4.1.0 pyhrv-0.4.1 shortuuid-1.0.13 spectrum-0.8.1\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:30:37.380265Z","iopub.execute_input":"2024-06-22T18:30:37.380675Z","iopub.status.idle":"2024-06-22T18:30:37.839037Z","shell.execute_reply.started":"2024-06-22T18:30:37.380642Z","shell.execute_reply":"2024-06-22T18:30:37.837844Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\npd.options.mode.chained_assignment = None\nimport warnings\nwarnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:34:58.192829Z","iopub.execute_input":"2024-06-22T18:34:58.193734Z","iopub.status.idle":"2024-06-22T18:34:58.203278Z","shell.execute_reply.started":"2024-06-22T18:34:58.193698Z","shell.execute_reply":"2024-06-22T18:34:58.201913Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport wfdb\nimport os\nimport ast\nimport neurokit2 as nk\nimport matplotlib\nimport matplotlib.pyplot as plt\npd.options.mode.chained_assignment = None","metadata":{"execution":{"iopub.status.busy":"2024-06-16T21:58:14.313285Z","iopub.execute_input":"2024-06-16T21:58:14.313969Z","iopub.status.idle":"2024-06-16T21:58:29.382538Z","shell.execute_reply.started":"2024-06-16T21:58:14.313924Z","shell.execute_reply":"2024-06-16T21:58:29.381266Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"2024-06-16 21:58:16.485877: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-06-16 21:58:16.486019: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-06-16 21:58:16.644434: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"code","source":"import warnings\nfrom scipy.interpolate import interp1d\nfrom scipy import signal\nfrom scipy.integrate import trapz\nimport pyhrv.tools as tools\nimport pyhrv.frequency_domain as fd\n\nwarnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)","metadata":{"execution":{"iopub.status.busy":"2024-06-16T21:58:29.384084Z","iopub.execute_input":"2024-06-16T21:58:29.385358Z","iopub.status.idle":"2024-06-16T21:58:29.802092Z","shell.execute_reply.started":"2024-06-16T21:58:29.385312Z","shell.execute_reply":"2024-06-16T21:58:29.800810Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"ecg_data_arrhytmia = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/df_arrhytmia.csv\", index_col=0)\necg_data_ptb_xl = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/df_ptb_xl.csv\", index_col=0)\n\nfor i in ecg_data_ptb_xl.index:\n    if ecg_data_ptb_xl.electrodes_problems[i] in ecg_data_ptb_xl.electrodes_problems.value_counts().index:\n        ecg_data_ptb_xl = ecg_data_ptb_xl.drop(labels = [i],axis = 0)\necg_data_ptb_xl =ecg_data_ptb_xl[(ecg_data_ptb_xl.diagnostic != \"\") & (ecg_data_ptb_xl.rhythm != \"\")]","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:31:16.647515Z","iopub.execute_input":"2024-06-22T18:31:16.647940Z","iopub.status.idle":"2024-06-22T18:31:46.032710Z","shell.execute_reply.started":"2024-06-22T18:31:16.647906Z","shell.execute_reply":"2024-06-22T18:31:46.031231Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"ecg_data_ptb_xl.electrodes_problems.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.489722Z","iopub.execute_input":"2024-06-07T10:36:54.490155Z","iopub.status.idle":"2024-06-07T10:36:54.501575Z","shell.execute_reply.started":"2024-06-07T10:36:54.490116Z","shell.execute_reply":"2024-06-07T10:36:54.500225Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"Series([], Name: count, dtype: int64)"},"metadata":{}}]},{"cell_type":"code","source":"def load_signals(df, path):\n    data = [wfdb.rdsamp(os.path.join(path, link)) for link in df.filename_hr]\n    data = np.array([signal for signal, meta in data])\n    return data","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.503005Z","iopub.execute_input":"2024-06-07T10:36:54.503375Z","iopub.status.idle":"2024-06-07T10:36:54.513943Z","shell.execute_reply.started":"2024-06-07T10:36:54.503343Z","shell.execute_reply":"2024-06-07T10:36:54.512777Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"ecg_signal = load_signals(ecg_data_ptb_xl, '/kaggle/input/ptb-xl-a-large-publicly-available2/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/')","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.515328Z","iopub.execute_input":"2024-06-07T10:36:54.515703Z","iopub.status.idle":"2024-06-07T10:36:54.527592Z","shell.execute_reply.started":"2024-06-07T10:36:54.515666Z","shell.execute_reply":"2024-06-07T10:36:54.526272Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"class ECG_RR():\n    def __init__(self,info,ecg_signals_1,name):\n        self.ECG = info\n        self.ecg_signals_1 = ecg_signals_1\n        self.name = name\n    def RR(self,sp,RR_d):\n        RR_d = RR_d\n        if len(self.ECG[self.name]) > 3:\n            for i in range(0,len(self.ECG[self.name])-1):\n                RR_d = sp(RR_d,list(self.ECG[self.name])[i+1] - list(self.ECG[self.name])[i])\n        else :RR_d =0\n        return RR_d\n    def R_m(self,sp):\n        if len(self.ecg_signals_1[self.ecg_signals_1[self.name]>0].ECG_Raw):\n            return sp(self.ecg_signals_1[self.ecg_signals_1[self.name]>0].ECG_Raw)\n        else: return 0       ","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.531619Z","iopub.execute_input":"2024-06-07T10:36:54.532024Z","iopub.status.idle":"2024-06-07T10:36:54.542029Z","shell.execute_reply.started":"2024-06-07T10:36:54.531986Z","shell.execute_reply":"2024-06-07T10:36:54.540964Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"def timedomain_pic(ecg_signals_1,info,name,results):\n    ECG = ECG_RR(info,ecg_signals_1,\"ECG_R_Peaks\")\n    name = str(name)+\"_\"\n    results[name+'Max_R'] = [ECG.R_m(max)]\n    results[name+'Min_R'] = [ECG.R_m(min)]\n    results[name+'Max_RR'] = [ECG.RR(max,0)*(1/500)]\n    results[name+'Min_RR'] = [ECG.RR(min,500)*(1/500)]\n    ECG = ECG_RR(info,ecg_signals_1,\"ECG_P_Peaks\")\n    results[name+'Max_P'] = [ECG.R_m(max)]\n    results[name+'Min_P'] = [ECG.R_m(min)]\n    ECG = ECG_RR(info,ecg_signals_1,\"ECG_Q_Peaks\")\n    results[name+'Max_Q'] = [ECG.R_m(max)]\n    results[name+'Min_Q'] = [ECG.R_m(min)]\n    ECG = ECG_RR(info,ecg_signals_1,\"ECG_S_Peaks\")\n    results[name+'Max_S'] = [ECG.R_m(max)]\n    results[name+'Min_S'] = [ECG.R_m(min)]\n    ECG = ECG_RR(info,ecg_signals_1,\"ECG_T_Peaks\")\n    results[name+'Max_T'] = [ECG.R_m(max)]\n    results[name+'Min_T'] = [ECG.R_m(min)]\n    return results","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.543331Z","iopub.execute_input":"2024-06-07T10:36:54.543685Z","iopub.status.idle":"2024-06-07T10:36:54.560390Z","shell.execute_reply.started":"2024-06-07T10:36:54.543656Z","shell.execute_reply":"2024-06-07T10:36:54.559100Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"def timedomain_HRV(ecg_signals_1,info,name,results):\n    try:\n        rr_intervals_list = info['ECG_R_Peaks']\n        nni_2 = tools.nn_intervals(rr_intervals_list)\n        result = fd.welch_psd(nni=nni_2,show=False, show_param=False,legend=False)\n        matplotlib.pyplot.close(fig=\"all\")\n    except:\n        return results\n    name = str(name)+\"_\"\n    results[name+'VLF_Peak (Hz)'] = [result[1][0]]\n    results[name+'LF_Peak (Hz)'] = [result[1][1]]\n    results[name+'HV_Peak (Hz)'] = [result[1][2]]\n    results[name+'VLF_Abs (ms2)'] = [result[2][0]]\n    results[name+'LF_Abs (ms2)'] = [result[2][1]]\n    results[name+'HV_Abs (ms2)'] = [result[2][2]]\n    results[name+'VLF_Rel (%)'] = [result[3][0]]\n    results[name+'LF_Rel (%)'] = [result[3][1]]\n    results[name+'HV_Rel (%)'] = [result[3][2]]\n    results[name+'VLF_Log (-)'] = [result[4][0]]\n    results[name+'LF_Log (-)'] = [result[4][1]]\n    results[name+'HV_Log (-)'] = [result[4][2]]\n    results[name+'LF_Norm (-)'] = [result[5][0]]\n    results[name+'HV_Norm (-)'] = [result[5][1]]\n    results[name+'LF/HF (-)'] = [result[6]]\n    results[name+'Total Power (ms)'] = [result[7]]\n    return results","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.562096Z","iopub.execute_input":"2024-06-07T10:36:54.563288Z","iopub.status.idle":"2024-06-07T10:36:54.575005Z","shell.execute_reply.started":"2024-06-07T10:36:54.563227Z","shell.execute_reply":"2024-06-07T10:36:54.573723Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"def timedomain(ecg_signals_1,info,name,results):\n    rr = info['ECG_R_Peaks'][:-1]\n    name = str(name)+\"_\"\n    rr_ecg = np.diff(rr)\n    rr = rr_ecg*10\n    hr = 60000/rr\n    results[name+'Mean RR (ms)'] = [np.mean(rr)]\n    results[name+'STD RR/SDNN (ms)'] = [np.std(rr)]\n    results[name+'Mean HR (Kubios\\' style) (beats/min)'] = [60000/np.mean(rr)]\n    results[name+'Mean HR (beats/min)'] = [np.mean(hr)]\n    results[name+'STD HR (beats/min)'] = [np.std(hr)]\n    results[name+'Min HR (beats/min)'] = [np.min(hr)]\n    results[name+'Max HR (beats/min)'] = [np.max(hr)]\n    results[name+'RMSSD (ms)'] = [np.sqrt(np.mean(np.square(np.diff(rr))))]\n    results[name+'NNxx'] = [np.sum(np.abs(np.diff(rr)) > 50)*1]\n    results[name+'pNNxx (%)'] = [100 * np.sum((np.abs(np.diff(rr)) > 50)*1) / len(rr)]\n    return results","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.576424Z","iopub.execute_input":"2024-06-07T10:36:54.576910Z","iopub.status.idle":"2024-06-07T10:36:54.593371Z","shell.execute_reply.started":"2024-06-07T10:36:54.576868Z","shell.execute_reply":"2024-06-07T10:36:54.591897Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"def ecg_HRV_df(ecg_signals_1,info,name):\n    results = {}\n    results = pd.DataFrame(results)\n    results =timedomain(ecg_signals_1,info,name,results)\n    results =timedomain_HRV(ecg_signals_1,info,name,results)\n    results =timedomain_pic(ecg_signals_1,info,name,results)\n    return results","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.595082Z","iopub.execute_input":"2024-06-07T10:36:54.595624Z","iopub.status.idle":"2024-06-07T10:36:54.606236Z","shell.execute_reply.started":"2024-06-07T10:36:54.595580Z","shell.execute_reply":"2024-06-07T10:36:54.604944Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"def ecg_HRV_0(ecg_signal):\n    df_HTV =pd.DataFrame()\n    df = pd.DataFrame(ecg_signal)\n    for j in range(0,12):\n        ecg_signals_1, info  = nk.ecg_process(df[j], sampling_rate=500)\n        HTV = ecg_HRV_df(ecg_signals_1,info,j)\n        df_HTV[HTV.columns] = HTV\n    return df_HTV","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.607896Z","iopub.execute_input":"2024-06-07T10:36:54.608371Z","iopub.status.idle":"2024-06-07T10:36:54.624988Z","shell.execute_reply.started":"2024-06-07T10:36:54.608328Z","shell.execute_reply":"2024-06-07T10:36:54.623683Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"from warnings import simplefilter\nsimplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)\n    \ndef ecg_HRV_1(k):\n    try:\n        ecg_signals_1, info  = nk.ecg_process(k, sampling_rate=500)\n        return 1\n    except:\n        return 0\n\ndef ecg_HRV(ecg_signal,df_HTV,nomer,nomer2):\n    for i in range(nomer,nomer2):\n        if (i/100).is_integer(): print(i)\n        df = pd.DataFrame(ecg_signal[i])\n        HTV_n = pd.DataFrame()\n        HTV = df_HTV[:1].copy()\n        for k in HTV.columns:\n                HTV[k] = 0\n                \n        for j in range(0,12):    \n            if ecg_HRV_1(df[j]) !=0:\n                ecg_signals_1, info  = nk.ecg_process(df[j], sampling_rate=500)\n                if (len(info['ECG_R_Peaks'][:-1]) > 3) :\n                    HTV = ecg_HRV_df(ecg_signals_1,info,j)           \n            HTV_n[HTV.columns] = HTV\n        df_HTV = pd.concat([df_HTV,HTV_n], ignore_index= True)\n            \n    return df_HTV","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.626402Z","iopub.execute_input":"2024-06-07T10:36:54.626754Z","iopub.status.idle":"2024-06-07T10:36:54.641138Z","shell.execute_reply.started":"2024-06-07T10:36:54.626724Z","shell.execute_reply":"2024-06-07T10:36:54.640003Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV  = ecg_HRV_0(ecg_signal[0])\necg_df_HTV  = ecg_HRV(ecg_signal[1:],ecg_df_HTV,len(ecg_df_HTV),len(ecg_signal))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV20000.csv\", index_col=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.642700Z","iopub.execute_input":"2024-06-07T10:36:54.643083Z","iopub.status.idle":"2024-06-07T10:36:54.654118Z","shell.execute_reply.started":"2024-06-07T10:36:54.643051Z","shell.execute_reply":"2024-06-07T10:36:54.653102Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV_2 = ecg_HRV(ecg_signal,ecg_df_HTV,len(ecg_df_HTV),len(ecg_signal))","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.655671Z","iopub.execute_input":"2024-06-07T10:36:54.656132Z","iopub.status.idle":"2024-06-07T10:36:54.667934Z","shell.execute_reply.started":"2024-06-07T10:36:54.656092Z","shell.execute_reply":"2024-06-07T10:36:54.666948Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV_2.to_csv(\"ecg_df_HTV\"+str(len(ecg_df_HTV_2))+\".csv\")#450/час","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.669331Z","iopub.execute_input":"2024-06-07T10:36:54.669709Z","iopub.status.idle":"2024-06-07T10:36:54.681371Z","shell.execute_reply.started":"2024-06-07T10:36:54.669678Z","shell.execute_reply":"2024-06-07T10:36:54.680310Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"#len(ecg_signal)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:36:54.682878Z","iopub.execute_input":"2024-06-07T10:36:54.683278Z","iopub.status.idle":"2024-06-07T10:36:54.695116Z","shell.execute_reply.started":"2024-06-07T10:36:54.683239Z","shell.execute_reply":"2024-06-07T10:36:54.693810Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV_2 = ecg_HRV(ecg_signal,ecg_df_HTV_2,len(ecg_df_HTV_2),len(ecg_df_HTV_2)+1000)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:12.609526Z","iopub.execute_input":"2024-05-28T18:13:12.609985Z","iopub.status.idle":"2024-05-28T18:13:12.620816Z","shell.execute_reply.started":"2024-05-28T18:13:12.609945Z","shell.execute_reply":"2024-05-28T18:13:12.619533Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV_2.to_csv(\"ecg_df_HTV\"+str(len(ecg_df_HTV_2))+\".csv\")#450/час","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:12.622082Z","iopub.execute_input":"2024-05-28T18:13:12.622469Z","iopub.status.idle":"2024-05-28T18:13:12.635373Z","shell.execute_reply.started":"2024-05-28T18:13:12.622431Z","shell.execute_reply":"2024-05-28T18:13:12.634165Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"## a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0","metadata":{}},{"cell_type":"code","source":"ecg_df_HTV = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV21728.csv\", index_col=0)\necg_df_HTV[\"rhythm\"] = list(ecg_data_ptb_xl.rhythm)\necg_df_HTV[\"diagnostic\"]=list(ecg_data_ptb_xl.diagnostic)\necg_df_HTV[\"age\"]=list(ecg_data_ptb_xl.age)\necg_df_HTV[\"sex\"]=list(ecg_data_ptb_xl.sex)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:31:46.034719Z","iopub.execute_input":"2024-06-22T18:31:46.035138Z","iopub.status.idle":"2024-06-22T18:31:48.666195Z","shell.execute_reply.started":"2024-06-22T18:31:46.035107Z","shell.execute_reply":"2024-06-22T18:31:48.664699Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV[\"rhythm\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:32:05.026241Z","iopub.execute_input":"2024-06-22T18:32:05.026682Z","iopub.status.idle":"2024-06-22T18:32:05.041275Z","shell.execute_reply.started":"2024-06-22T18:32:05.026648Z","shell.execute_reply":"2024-06-22T18:32:05.039881Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"rhythm\nSR       16721\nAFIB      1495\nSTACH      819\nSARRH      765\nSBRAD      633\nAFLT        40\nSVTAC       18\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"ecg_data_arrhytmia = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/df_arrhytmia.csv\", index_col=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:32:07.228901Z","iopub.execute_input":"2024-06-22T18:32:07.229350Z","iopub.status.idle":"2024-06-22T18:32:07.331663Z","shell.execute_reply.started":"2024-06-22T18:32:07.229319Z","shell.execute_reply":"2024-06-22T18:32:07.330620Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"ecg_data_arrhytmia","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:32:08.525709Z","iopub.execute_input":"2024-06-22T18:32:08.526162Z","iopub.status.idle":"2024-06-22T18:32:08.555275Z","shell.execute_reply.started":"2024-06-22T18:32:08.526128Z","shell.execute_reply":"2024-06-22T18:32:08.554033Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"       ecg_id filename_path filename   Age     Sex  \\\n0           1       01/010/  JS00001  85.0    Male   \n1           2       01/010/  JS00002  59.0  Female   \n2           4       01/010/  JS00004  66.0    Male   \n3           5       01/010/  JS00005  73.0  Female   \n4           6       01/010/  JS00006  46.0  Female   \n...       ...           ...      ...   ...     ...   \n44978   45378       45/459/  JS45378  50.0    Male   \n44979   45379       45/459/  JS45379  64.0    Male   \n44980   45380       45/459/  JS45380  61.0  Female   \n44981   45381       45/459/  JS45381  46.0  Female   \n45046   45446       46/460/  JS45446  89.0    Male   \n\n                                                    Dx             diagnos  \\\n0                         164889003,59118001,164934002       AFIB,RBBB,TWC   \n1                                  426177001,164934002           SBRAD,TWC   \n2                                            426177001               SBRAD   \n3                        164890007,429622005,428750005      AFLT,STDD,STTC   \n4                                            426177001               SBRAD   \n...                                                ...                 ...   \n44978                               426761007,55930002               SVTAC   \n44979                    164930006,426761007,429622005      STE,SVTAC,STDD   \n44980  429622005,59931005,164934002,426761007,55930002  STDD,TWO,TWC,SVTAC   \n44981                                        426761007               SVTAC   \n45046                     164873001,55930002,164896001                 LVH   \n\n      rhythm diagnostic  \n0       AFIB        NaN  \n1      SBRAD        NaN  \n2      SBRAD        NaN  \n3       AFLT        NaN  \n4      SBRAD        NaN  \n...      ...        ...  \n44978  SVTAC        NaN  \n44979  SVTAC        NaN  \n44980  SVTAC        NaN  \n44981  SVTAC        NaN  \n45046    NaN        LVH  \n\n[42455 rows x 9 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ecg_id</th>\n      <th>filename_path</th>\n      <th>filename</th>\n      <th>Age</th>\n      <th>Sex</th>\n      <th>Dx</th>\n      <th>diagnos</th>\n      <th>rhythm</th>\n      <th>diagnostic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>01/010/</td>\n      <td>JS00001</td>\n      <td>85.0</td>\n      <td>Male</td>\n      <td>164889003,59118001,164934002</td>\n      <td>AFIB,RBBB,TWC</td>\n      <td>AFIB</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>01/010/</td>\n      <td>JS00002</td>\n      <td>59.0</td>\n      <td>Female</td>\n      <td>426177001,164934002</td>\n      <td>SBRAD,TWC</td>\n      <td>SBRAD</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>01/010/</td>\n      <td>JS00004</td>\n      <td>66.0</td>\n      <td>Male</td>\n      <td>426177001</td>\n      <td>SBRAD</td>\n      <td>SBRAD</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>01/010/</td>\n      <td>JS00005</td>\n      <td>73.0</td>\n      <td>Female</td>\n      <td>164890007,429622005,428750005</td>\n      <td>AFLT,STDD,STTC</td>\n      <td>AFLT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>01/010/</td>\n      <td>JS00006</td>\n      <td>46.0</td>\n      <td>Female</td>\n      <td>426177001</td>\n      <td>SBRAD</td>\n      <td>SBRAD</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>44978</th>\n      <td>45378</td>\n      <td>45/459/</td>\n      <td>JS45378</td>\n      <td>50.0</td>\n      <td>Male</td>\n      <td>426761007,55930002</td>\n      <td>SVTAC</td>\n      <td>SVTAC</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>44979</th>\n      <td>45379</td>\n      <td>45/459/</td>\n      <td>JS45379</td>\n      <td>64.0</td>\n      <td>Male</td>\n      <td>164930006,426761007,429622005</td>\n      <td>STE,SVTAC,STDD</td>\n      <td>SVTAC</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>44980</th>\n      <td>45380</td>\n      <td>45/459/</td>\n      <td>JS45380</td>\n      <td>61.0</td>\n      <td>Female</td>\n      <td>429622005,59931005,164934002,426761007,55930002</td>\n      <td>STDD,TWO,TWC,SVTAC</td>\n      <td>SVTAC</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>44981</th>\n      <td>45381</td>\n      <td>45/459/</td>\n      <td>JS45381</td>\n      <td>46.0</td>\n      <td>Female</td>\n      <td>426761007</td>\n      <td>SVTAC</td>\n      <td>SVTAC</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>45046</th>\n      <td>45446</td>\n      <td>46/460/</td>\n      <td>JS45446</td>\n      <td>89.0</td>\n      <td>Male</td>\n      <td>164873001,55930002,164896001</td>\n      <td>LVH</td>\n      <td>NaN</td>\n      <td>LVH</td>\n    </tr>\n  </tbody>\n</table>\n<p>42455 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"ecg_data_arrhytmia.rhythm.value_counts().index[:6]","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:32:11.891233Z","iopub.execute_input":"2024-06-22T18:32:11.891657Z","iopub.status.idle":"2024-06-22T18:32:11.908007Z","shell.execute_reply.started":"2024-06-22T18:32:11.891625Z","shell.execute_reply":"2024-06-22T18:32:11.906652Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"Index(['SBRAD', 'SR', 'AFLT', 'STACH', 'AFIB', 'SVTAC'], dtype='object', name='rhythm')"},"metadata":{}}]},{"cell_type":"code","source":"ecg_data_arrhytmia.diagnostic.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:32:34.375821Z","iopub.execute_input":"2024-06-22T18:32:34.376890Z","iopub.status.idle":"2024-06-22T18:32:34.388431Z","shell.execute_reply.started":"2024-06-22T18:32:34.376853Z","shell.execute_reply":"2024-06-22T18:32:34.387098Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"diagnostic\n1AVB            1119\nLVH              612\nRVH               98\nWPW               72\n3AVB              69\n2AVB              58\n1AVB,LVH          15\nLVH,2AVB           5\nRVH,LVH            5\n3AVB,LVH           4\n1AVB,2AVB          3\nRVH,3AVB           3\nRVH,1AVB           2\nRVH,1AVB,LVH       1\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"for i in  ecg_data_arrhytmia.index:\n    if (ecg_data_arrhytmia.rhythm[i] in ecg_df_HTV[\"rhythm\"].value_counts().index) != True:\n        ecg_data_arrhytmia.rhythm[i] = \"\"","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:36:15.044501Z","iopub.execute_input":"2024-06-22T18:36:15.045493Z","iopub.status.idle":"2024-06-22T18:36:15.050667Z","shell.execute_reply.started":"2024-06-22T18:36:15.045447Z","shell.execute_reply":"2024-06-22T18:36:15.049354Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"ecg_data_arrhytmia.rhythm.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:34:58.173129Z","iopub.execute_input":"2024-06-22T18:34:58.173489Z","iopub.status.idle":"2024-06-22T18:34:58.191146Z","shell.execute_reply.started":"2024-06-22T18:34:58.173458Z","shell.execute_reply":"2024-06-22T18:34:58.189733Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"rhythm\nSBRAD    16552\nSR        8099\nAFLT      7955\nSTACH     7239\nAFIB      1780\nSVTAC      648\n           182\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"def load_signals_data_arrhytmia(df, path):\n    data = [wfdb.rdsamp(os.path.join(path + str(df.filename_path[link]), df.filename[link])) for link in df.index ]\n    data = np.array([signal for signal, meta in data])\n    return data","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.210978Z","iopub.execute_input":"2024-05-28T18:13:16.211367Z","iopub.status.idle":"2024-05-28T18:13:16.221570Z","shell.execute_reply.started":"2024-05-28T18:13:16.211337Z","shell.execute_reply":"2024-05-28T18:13:16.220434Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"path_arrhytmia = \"/kaggle/input/chapman-ecg-database/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/WFDBRecords/\"\necg_signal_arrhytmia = load_signals_data_arrhytmia(ecg_data_new_arrhytmia_AFLT,path_arrhytmia )","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.222972Z","iopub.execute_input":"2024-05-28T18:13:16.223353Z","iopub.status.idle":"2024-05-28T18:13:16.232442Z","shell.execute_reply.started":"2024-05-28T18:13:16.223315Z","shell.execute_reply":"2024-05-28T18:13:16.231279Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"ecg_data_new_arrhytmia_SVTAC = ecg_data_arrhytmia[(ecg_data_arrhytmia.rhythm == \"SVTAC\")]","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.233840Z","iopub.execute_input":"2024-05-28T18:13:16.234326Z","iopub.status.idle":"2024-05-28T18:13:16.243729Z","shell.execute_reply.started":"2024-05-28T18:13:16.234285Z","shell.execute_reply":"2024-05-28T18:13:16.242486Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"ecg_data_new_arrhytmia_SVTAC.index = pd.RangeIndex(start=1, stop=len(ecg_data_new_arrhytmia_SVTAC) + 1)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.245326Z","iopub.execute_input":"2024-05-28T18:13:16.245705Z","iopub.status.idle":"2024-05-28T18:13:16.257411Z","shell.execute_reply.started":"2024-05-28T18:13:16.245672Z","shell.execute_reply":"2024-05-28T18:13:16.256274Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"len(ecg_data_arrhytmia[(ecg_data_arrhytmia.rhythm == \"AFLT\")])","metadata":{}},{"cell_type":"code","source":"ecg_data_new_arrhytmia_AFLT = ecg_data_arrhytmia[(ecg_data_arrhytmia.rhythm == \"AFLT\")]\necg_data_new_arrhytmia_AFLT = ecg_data_new_arrhytmia_AFLT.sample(n=1500)\necg_data_new_arrhytmia_AFLT = ecg_data_new_arrhytmia_AFLT.sort_values(\"ecg_id\")\necg_data_new_arrhytmia_AFLT.index = pd.RangeIndex(start=1, stop=len(ecg_data_new_arrhytmia_AFLT) + 1)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.258705Z","iopub.execute_input":"2024-05-28T18:13:16.259024Z","iopub.status.idle":"2024-05-28T18:13:16.270205Z","shell.execute_reply.started":"2024-05-28T18:13:16.258992Z","shell.execute_reply":"2024-05-28T18:13:16.268972Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"path_arrhytmia = \"/kaggle/input/chapman-ecg-database/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/WFDBRecords/\"\necg_signal_arrhytmia = load_signals_data_arrhytmia(ecg_data_new_arrhytmia_AFLT,path_arrhytmia )","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.271711Z","iopub.execute_input":"2024-05-28T18:13:16.272073Z","iopub.status.idle":"2024-05-28T18:13:16.287853Z","shell.execute_reply.started":"2024-05-28T18:13:16.272042Z","shell.execute_reply":"2024-05-28T18:13:16.286756Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"df_HTV = ecg_HRV_0(ecg_signal_arrhytmia[0])\ndf_HTV = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_arrhytmia7000.csv\", index_col=0)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.289396Z","iopub.execute_input":"2024-05-28T18:13:16.289766Z","iopub.status.idle":"2024-05-28T18:13:16.299929Z","shell.execute_reply.started":"2024-05-28T18:13:16.289736Z","shell.execute_reply":"2024-05-28T18:13:16.298715Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"df_HTV_AFLT = ecg_HRV(ecg_signal_arrhytmia,df_HTV[:1],0,len(ecg_data_new_arrhytmia_AFLT))","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.301521Z","iopub.execute_input":"2024-05-28T18:13:16.302584Z","iopub.status.idle":"2024-05-28T18:13:16.311567Z","shell.execute_reply.started":"2024-05-28T18:13:16.302538Z","shell.execute_reply":"2024-05-28T18:13:16.310328Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"df_HTV_AFLT[['ecg_id', 'age', 'sex','rhythm', 'diagnostic']] = ecg_data_new_arrhytmia_AFLT[['ecg_id', 'age', 'sex','rhythm', 'diagnostic']]\ndf_HTV_AFLT = df_HTV_AFLT.drop(labels = [0],axis = 0)\ndf_HTV_AFLT = df_HTV_AFLT.rename(columns={'Age': 'age', 'Sex': 'sex'})\ndf_HTV_AFLT[\"sex\"] = df_HTV_AFLT[\"sex\"].replace({\"Female\":1,\"Male\":0})","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.312972Z","iopub.execute_input":"2024-05-28T18:13:16.313848Z","iopub.status.idle":"2024-05-28T18:13:16.323313Z","shell.execute_reply.started":"2024-05-28T18:13:16.313814Z","shell.execute_reply":"2024-05-28T18:13:16.322164Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"df_HTV_AFLT.to_csv(\"ecg_df_HTV_AFLT\"+str(len(df_HTV_AFLT))+\".csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.324576Z","iopub.execute_input":"2024-05-28T18:13:16.324905Z","iopub.status.idle":"2024-05-28T18:13:16.334480Z","shell.execute_reply.started":"2024-05-28T18:13:16.324878Z","shell.execute_reply":"2024-05-28T18:13:16.333244Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"for i in range(0,int((len(ecg_signal_arrhytmia)-len(df_HTV))/1000)):\n    df_HTV = ecg_HRV(ecg_signal_arrhytmia,df_HTV,len(df_HTV),len(df_HTV) + 1000)\n    df_HTV.to_csv(\"ecg_df_HTV_arrhytmia\"+str(len(df_HTV))+\".csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:16.335794Z","iopub.execute_input":"2024-05-28T18:13:16.336162Z","iopub.status.idle":"2024-05-28T18:13:16.348031Z","shell.execute_reply.started":"2024-05-28T18:13:16.336131Z","shell.execute_reply":"2024-05-28T18:13:16.346807Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"markdown","source":"## mimic 4","metadata":{}},{"cell_type":"code","source":"record_list = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/mimic/record_list.csv\", index_col=0)\nd_measurements = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/d_measurements.csv\", index_col=0)\necg_df_HTV_mimic = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_mimic_18.csv\", index_col=0)\nlen(d_measurements),len(ecg_df_HTV_mimic)","metadata":{"execution":{"iopub.status.busy":"2024-05-26T03:44:58.595444Z","iopub.execute_input":"2024-05-26T03:44:58.596178Z","iopub.status.idle":"2024-05-26T03:45:01.887095Z","shell.execute_reply.started":"2024-05-26T03:44:58.596142Z","shell.execute_reply":"2024-05-26T03:45:01.885999Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"(18811, 18811)"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n!pip install wget\nimport wget","metadata":{"execution":{"iopub.status.busy":"2024-05-28T03:13:34.974896Z","iopub.execute_input":"2024-05-28T03:13:34.975357Z","iopub.status.idle":"2024-05-28T03:13:52.804944Z","shell.execute_reply.started":"2024-05-28T03:13:34.975313Z","shell.execute_reply":"2024-05-28T03:13:52.803164Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Collecting wget\n  Downloading wget-3.2.zip (10 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hBuilding wheels for collected packages: wget\n  Building wheel for wget (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9655 sha256=5bc3f593f3ae9d89581659be38ce9c28bd3102b0024d6fa8610a37dace0b08ec\n  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\nSuccessfully built wget\nInstalling collected packages: wget\nSuccessfully installed wget-3.2\n","output_type":"stream"}]},{"cell_type":"code","source":"d_measurements.index = pd.RangeIndex(start=0, stop=len(d_measurements) )","metadata":{"execution":{"iopub.status.busy":"2024-05-26T03:45:01.889221Z","iopub.execute_input":"2024-05-26T03:45:01.889667Z","iopub.status.idle":"2024-05-26T03:45:01.895199Z","shell.execute_reply.started":"2024-05-26T03:45:01.889627Z","shell.execute_reply":"2024-05-26T03:45:01.894101Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic[\"diagnostic\"] = d_measurements[\"report\"]\necg_df_HTV_mimic[\"rhythm\"] = d_measurements[\"report_0\"]","metadata":{"execution":{"iopub.status.busy":"2024-05-26T03:29:24.379820Z","iopub.execute_input":"2024-05-26T03:29:24.380247Z","iopub.status.idle":"2024-05-26T03:29:24.397854Z","shell.execute_reply.started":"2024-05-26T03:29:24.380210Z","shell.execute_reply":"2024-05-26T03:29:24.396703Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic.to_csv(\"ecg_df_HTV_mimic.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-10T08:16:05.795031Z","iopub.execute_input":"2024-05-10T08:16:05.795466Z","iopub.status.idle":"2024-05-10T08:16:16.644586Z","shell.execute_reply.started":"2024-05-10T08:16:05.795415Z","shell.execute_reply":"2024-05-10T08:16:16.643516Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"def load_signals_mimic(df):\n    data = [wfdb.rdsamp(str(df.name_file[link]), pn_dir='mimic-iv-ecg/1.0/'+ df.path[link] + \"/\") for link in df.index]\n    data = np.array([signal for signal, meta in data])\n    return data\necg_signal = load_signals_mimic(d_measurements)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:13:27.032609Z","iopub.execute_input":"2024-05-28T18:13:27.033062Z","iopub.status.idle":"2024-05-28T18:13:27.039832Z","shell.execute_reply.started":"2024-05-28T18:13:27.033021Z","shell.execute_reply":"2024-05-28T18:13:27.038760Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":"8 мин  - \n1 000","metadata":{}},{"cell_type":"code","source":"HRV = ecg_HRV_0(wfdb.rdsamp(d_measurements.name_file[0], pn_dir='mimic-iv-ecg/1.0/'+ d_measurements.path[0] + \"/\")[0])","metadata":{"execution":{"iopub.status.busy":"2024-05-04T16:55:06.949604Z","iopub.execute_input":"2024-05-04T16:55:06.949945Z","iopub.status.idle":"2024-05-04T16:55:11.380185Z","shell.execute_reply.started":"2024-05-04T16:55:06.949917Z","shell.execute_reply":"2024-05-04T16:55:11.379128Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"#HRV_1 = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_mimic1000.csv\", index_col=0)","metadata":{"execution":{"iopub.status.busy":"2024-05-05T15:29:16.339773Z","iopub.execute_input":"2024-05-05T15:29:16.340340Z","iopub.status.idle":"2024-05-05T15:29:16.540929Z","shell.execute_reply.started":"2024-05-05T15:29:16.340306Z","shell.execute_reply":"2024-05-05T15:29:16.539800Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"HRV_1 = ecg_HRV(ecg_signal[1:],HRV,len(HRV),len(ecg_signal))","metadata":{"execution":{"iopub.status.busy":"2024-05-04T16:55:11.382117Z","iopub.execute_input":"2024-05-04T16:55:11.382535Z","iopub.status.idle":"2024-05-04T18:36:19.271495Z","shell.execute_reply.started":"2024-05-04T16:55:11.382497Z","shell.execute_reply":"2024-05-04T18:36:19.270307Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"100\n200\n300\n400\n500\n600\n700\n800\n900\n","output_type":"stream"}]},{"cell_type":"markdown","source":"1. 21\n1. 48 - 300\n","metadata":{}},{"cell_type":"code","source":"#HRV_1 = pd.concat([HRV_1,\n#                 ecg_HRV(ecg_signal[:1000],ecg_HRV_0(ecg_signal[0]),1,len(ecg_signal))\n#                 ])","metadata":{"execution":{"iopub.status.busy":"2024-05-05T15:29:16.552038Z","iopub.execute_input":"2024-05-05T15:29:16.552664Z","iopub.status.idle":"2024-05-05T16:58:36.122792Z","shell.execute_reply.started":"2024-05-05T15:29:16.552630Z","shell.execute_reply":"2024-05-05T16:58:36.121139Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"100\n200\n300\n400\n500\n600\n700\n800\n900\n","output_type":"stream"}]},{"cell_type":"code","source":"HRV_1.to_csv(\"ecg_df_HTV_mimic.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-05T16:58:36.124742Z","iopub.execute_input":"2024-05-05T16:58:36.125498Z","iopub.status.idle":"2024-05-05T16:58:37.586722Z","shell.execute_reply.started":"2024-05-05T16:58:36.125458Z","shell.execute_reply":"2024-05-05T16:58:37.585635Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"record_list = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/mimic/record_list.csv\", index_col=0)\nd_measurements = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/d_measurements.csv\", index_col=0)\necg_df_HTV_mimic = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_mimic.csv\", index_col=0)\nd_measurements.index = pd.RangeIndex(start=0, stop=len(d_measurements) )\necg_df_HTV_mimic.index = pd.RangeIndex(start=0, stop=len(ecg_df_HTV_mimic) )\necg_df_HTV_mimic[\"diagnostic\"] = d_measurements.report\necg_df_HTV_mimic[\"rhythm\"] = d_measurements.report_0\necg_df_HTV_mimic.diagnostic.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:39:31.255839Z","iopub.execute_input":"2024-06-22T18:39:31.256870Z","iopub.status.idle":"2024-06-22T18:39:36.763489Z","shell.execute_reply.started":"2024-06-22T18:39:31.256827Z","shell.execute_reply":"2024-06-22T18:39:36.762362Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"diagnostic\nCRBBB      2000\nLAFB       2000\nLVH        2000\nLAO/LAE    2000\nCLBBB      2000\n1AVB       2000\nNST_       2000\nNDT        1160\nRVH        1025\nIRBBB       573\nISCAL       465\nLPFB        410\nILBBB       406\nISCLA       311\nRAO/RAE     245\n2AVB        216\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"ecg_df_HTV_mimic_N = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/HRV0-10000.csv\", index_col=0)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:14:48.056912Z","iopub.execute_input":"2024-05-28T18:14:48.057492Z","iopub.status.idle":"2024-05-28T18:14:50.325667Z","shell.execute_reply.started":"2024-05-28T18:14:48.057451Z","shell.execute_reply":"2024-05-28T18:14:50.324160Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"ecg_signal = load_signals_mimic(d_measurements[10000:])","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:15:14.668225Z","iopub.execute_input":"2024-05-28T18:15:14.668718Z","iopub.status.idle":"2024-05-28T18:21:53.935632Z","shell.execute_reply.started":"2024-05-28T18:15:14.668678Z","shell.execute_reply":"2024-05-28T18:21:53.934063Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic_N = ecg_df_HTV_mimic.copy()","metadata":{"execution":{"iopub.status.busy":"2024-05-26T17:42:04.346436Z","iopub.execute_input":"2024-05-26T17:42:04.346877Z","iopub.status.idle":"2024-05-26T17:42:04.385551Z","shell.execute_reply.started":"2024-05-26T17:42:04.346836Z","shell.execute_reply":"2024-05-26T17:42:04.384588Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"import time","metadata":{"execution":{"iopub.status.busy":"2024-05-27T23:21:18.415344Z","iopub.execute_input":"2024-05-27T23:21:18.415911Z","iopub.status.idle":"2024-05-27T23:21:18.425790Z","shell.execute_reply.started":"2024-05-27T23:21:18.415867Z","shell.execute_reply":"2024-05-27T23:21:18.424307Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"def ecg_HRV_1(k):\n    try:\n        ecg_signals_1, info  = nk.ecg_process(k, sampling_rate=500)\n        return 1\n    except:\n        return 0\n\ndef f_0_1(indeks,ecg_signal,ecg_df_HTV_mimic):\n    results = {}\n    results = pd.DataFrame(results)\n    results_n = pd.DataFrame()\n    columns_r = []\n    for i in range(len(ecg_signal)):\n        df = pd.DataFrame(ecg_signal[i])\n        for j in range(0,12):\n            if ecg_HRV_1(df[j]) != 0:\n                ecg_signals_1, info  = nk.ecg_process(df[j], sampling_rate=500)\n                df_n = timedomain_HRV(ecg_signals_1,info,j,results)\n            else:\n                df_n = ecg_df_HTV_mimic[columns_r][len(ecg_df_HTV_mimic)-1:len(ecg_df_HTV_mimic)]\n        results_n = pd.concat([\n            results_n,df_n\n        ])\n        columns_r =results_n.columns\n    results_n.index = pd.RangeIndex(start=indeks, stop=indeks+len(results_n) )\n    #ecg_df_HTV_mimic[df_n.columns][] = df_n\n    return results_n \n","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:21:53.938275Z","iopub.execute_input":"2024-05-28T18:21:53.938735Z","iopub.status.idle":"2024-05-28T18:21:53.949233Z","shell.execute_reply.started":"2024-05-28T18:21:53.938697Z","shell.execute_reply":"2024-05-28T18:21:53.947804Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"markdown","source":"19.57","metadata":{}},{"cell_type":"code","source":"results  = f_0_1(18000,ecg_signal,ecg_df_HTV_mimic)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T18:21:53.950634Z","iopub.execute_input":"2024-05-28T18:21:53.951079Z","iopub.status.idle":"2024-05-28T19:29:15.655200Z","shell.execute_reply.started":"2024-05-28T18:21:53.951043Z","shell.execute_reply":"2024-05-28T19:29:15.653734Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"results = pd.concat([ecg_df_HTV_mimic_N[results.columns][:18000]\n          ,results])","metadata":{"execution":{"iopub.status.busy":"2024-05-28T19:29:15.658015Z","iopub.execute_input":"2024-05-28T19:29:15.658387Z","iopub.status.idle":"2024-05-28T19:29:15.706518Z","shell.execute_reply.started":"2024-05-28T19:29:15.658357Z","shell.execute_reply":"2024-05-28T19:29:15.704980Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"results","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic_N[results.columns] = results","metadata":{"execution":{"iopub.status.busy":"2024-05-28T19:29:15.708205Z","iopub.execute_input":"2024-05-28T19:29:15.708586Z","iopub.status.idle":"2024-05-28T19:29:15.772805Z","shell.execute_reply.started":"2024-05-28T19:29:15.708555Z","shell.execute_reply":"2024-05-28T19:29:15.771221Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"HRV_0 = ecg_df_HTV_mimic_N[18000:].copy()\nfor col in HRV_0.columns:\n    pct_missing = np.mean(HRV_0[col].isnull())\n    if round(pct_missing*100) > 5:\n        print('{} - {}%'.format(col, round(pct_missing*100)),pct_missing)","metadata":{"execution":{"iopub.status.busy":"2024-05-28T19:29:15.774716Z","iopub.execute_input":"2024-05-28T19:29:15.775277Z","iopub.status.idle":"2024-05-28T19:29:15.879749Z","shell.execute_reply.started":"2024-05-28T19:29:15.775222Z","shell.execute_reply":"2024-05-28T19:29:15.878491Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic_N1 = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/HRV0-10000.csv\", index_col=0)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic_N.to_csv(\"HRV15000-16000.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-28T05:03:47.483382Z","iopub.execute_input":"2024-05-28T05:03:47.483762Z","iopub.status.idle":"2024-05-28T05:03:59.595917Z","shell.execute_reply.started":"2024-05-28T05:03:47.483730Z","shell.execute_reply":"2024-05-28T05:03:59.594546Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic  =pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_mimic.csv\", index_col=0)\nrecord_list = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/mimic/record_list.csv\", index_col=0)\nd_measurements = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/d_measurements.csv\", index_col=0)\nd_measurements.index = pd.RangeIndex(start=0, stop=len(d_measurements) )\necg_df_HTV_mimic.index = pd.RangeIndex(start=0, stop=len(ecg_df_HTV_mimic) )\necg_df_HTV_mimic[\"diagnostic\"] = d_measurements.report\necg_df_HTV_mimic[\"rhythm\"] = d_measurements.report_0\necg_df_HTV_mimic[\"rhythm\"] = ecg_df_HTV_mimic[\"rhythm\"].replace({\"sinus rhythm\":\"SR\",\"sinus bradycardia\":\"SBRAD\",\"sinus tachycardia\":\"STACH\",\"atrial fibrillation\":\"AFIB\",\"atrial flutter\":\"AFLT\",\"supraventricular tachycardia\":\"SVTAC\"})\necg_df_HTV_mimic.rhythm.value_counts()[:10]","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:40:08.476672Z","iopub.execute_input":"2024-06-22T18:40:08.477108Z","iopub.status.idle":"2024-06-22T18:40:12.736927Z","shell.execute_reply.started":"2024-06-22T18:40:08.477075Z","shell.execute_reply":"2024-06-22T18:40:12.735773Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"rhythm\nSR                                                         8985\nSBRAD                                                      1443\nSTACH                                                      1436\nAFIB                                                       1224\n*** consider acute st elevation mi ***                      878\n--- warning: data quality may affect interpretation ---     462\nsinus arrhythmia                                            275\nsinus rhythm with 1st degree a-v block                      247\natrial fibrillation with rapid ventricular response         244\nsinus rhythm with borderline 1st degree a-v block           236\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"def func_poisk_name(osn,osn_1,iskl ):\n    report = \"rhythm\"\n    report_1 = pd.DataFrame({\"index\" : ecg_df_HTV_mimic[report].value_counts().to_frame().index ,\"counts\" : list(ecg_df_HTV_mimic[report].value_counts())})\n    l_new = report_1[report_1[\"index\"].str.contains(osn)]\n    for i in osn_1:\n        l_new = l_new[l_new[\"index\"].str.contains(i)]\n    for i in iskl:\n        l_new = l_new[(l_new[\"index\"].str.contains(i))!=True]\n    l_new = list(l_new[:10][\"index\"])\n    return l_new","metadata":{"execution":{"iopub.status.busy":"2024-06-02T13:35:21.880523Z","iopub.execute_input":"2024-06-02T13:35:21.881394Z","iopub.status.idle":"2024-06-02T13:35:21.889123Z","shell.execute_reply.started":"2024-06-02T13:35:21.881357Z","shell.execute_reply":"2024-06-02T13:35:21.887822Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"def f_1(df,name,name_1,name_2,n):\n    df[\"rhythm\"] = df[\"rhythm\"].replace({name:n})\n    for j in func_poisk_name(name,name_1,name_2):\n        df[\"rhythm\"] = df[\"rhythm\"].replace({j:n})\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:40:39.746986Z","iopub.execute_input":"2024-06-22T18:40:39.747709Z","iopub.status.idle":"2024-06-22T18:40:39.769394Z","shell.execute_reply.started":"2024-06-22T18:40:39.747658Z","shell.execute_reply":"2024-06-22T18:40:39.766045Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"scp_statements = pd.read_csv(\"/kaggle/input/ptb-xl-a-large-publicly-available2/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/scp_statements.csv\")\nscp_statements = scp_statements[scp_statements[\"rhythm\"] == 1]","metadata":{"execution":{"iopub.status.busy":"2024-06-02T13:35:36.032801Z","iopub.execute_input":"2024-06-02T13:35:36.033199Z","iopub.status.idle":"2024-06-02T13:35:36.052120Z","shell.execute_reply.started":"2024-06-02T13:35:36.033170Z","shell.execute_reply":"2024-06-02T13:35:36.051099Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"for i in scp_statements.index:\n    ecg_df_HTV_mimic = f_1(ecg_df_HTV_mimic,scp_statements.description[i],[],[],scp_statements[\"Unnamed: 0\"][i])\n","metadata":{"execution":{"iopub.status.busy":"2024-06-02T13:35:43.300040Z","iopub.execute_input":"2024-06-02T13:35:43.300421Z","iopub.status.idle":"2024-06-02T13:35:43.690055Z","shell.execute_reply.started":"2024-06-02T13:35:43.300393Z","shell.execute_reply":"2024-06-02T13:35:43.688788Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2239475601.py:4: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n  l_new = report_1[report_1[\"index\"].str.contains(osn)]\n/tmp/ipykernel_33/2239475601.py:4: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n  l_new = report_1[report_1[\"index\"].str.contains(osn)]\n","output_type":"stream"}]},{"cell_type":"code","source":"ecg_df_HTV_mimic.rhythm.value_counts()[:100]","metadata":{"execution":{"iopub.status.busy":"2024-06-22T18:40:31.290281Z","iopub.execute_input":"2024-06-22T18:40:31.290677Z","iopub.status.idle":"2024-06-22T18:40:31.305328Z","shell.execute_reply.started":"2024-06-22T18:40:31.290647Z","shell.execute_reply":"2024-06-22T18:40:31.303859Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"rhythm\nSR                                        8985\nSBRAD                                     1443\nSTACH                                     1436\nAFIB                                      1224\n*** consider acute st elevation mi ***     878\n                                          ... \nmarked sinus bradycardia                     8\nwide qrs tachycardia                         7\nsinus arrhythmia with frequent pvcs          7\nsinus arrhythmia with pvcs                   7\nsecond degree av block, mobitz ii            7\nName: count, Length: 100, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"ecg_df_HTV_mimic.to_csv(\"ecg_df_HTV_mimic.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-06-02T13:36:50.164422Z","iopub.execute_input":"2024-06-02T13:36:50.164849Z","iopub.status.idle":"2024-06-02T13:37:06.095188Z","shell.execute_reply.started":"2024-06-02T13:36:50.164792Z","shell.execute_reply":"2024-06-02T13:37:06.093870Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_mimic","metadata":{"execution":{"iopub.status.busy":"2024-06-02T13:37:06.097197Z","iopub.execute_input":"2024-06-02T13:37:06.097642Z","iopub.status.idle":"2024-06-02T13:37:06.161242Z","shell.execute_reply.started":"2024-06-02T13:37:06.097601Z","shell.execute_reply":"2024-06-02T13:37:06.159798Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"       0_Mean RR (ms)  0_STD RR/SDNN (ms)  \\\n0         4407.777778          875.249524   \n1         3547.500000          220.949655   \n2         3980.000000           34.641016   \n3         4497.777778           29.355211   \n4         4595.555556           24.545247   \n...               ...                 ...   \n18806     2828.000000          808.498196   \n18807     4086.666667          749.192158   \n18808     5265.714286         2308.467996   \n18809     2202.000000           34.438351   \n18810     2396.111111            5.905637   \n\n       0_Mean HR (Kubios' style) (beats/min)  0_Mean HR (beats/min)  \\\n0                                  13.612301              14.200225   \n1                                  16.913319              16.976444   \n2                                  15.075377              15.076531   \n3                                  13.339921              13.340492   \n4                                  13.056093              13.056467   \n...                                      ...                    ...   \n18806                              21.216407              22.524990   \n18807                              14.681892              15.221731   \n18808                              11.394466              13.884246   \n18809                              27.247956              27.254547   \n18810                              25.040575              25.040727   \n\n       0_STD HR (beats/min)  0_Min HR (beats/min)  0_Max HR (beats/min)  \\\n0                  2.994709             10.507881             19.292605   \n1                  1.015075             15.151515             18.292683   \n2                  0.132545             14.962594             15.345269   \n3                  0.087428             13.245033             13.483146   \n4                  0.069966             12.958963             13.186813   \n...                     ...                   ...                   ...   \n18806              4.600528             13.245033             26.315789   \n18807              2.972090             12.096774             20.689655   \n18808              5.884549              7.050529             21.428571   \n18809              0.421615             25.974026             28.037383   \n18810              0.061661             24.896266             25.104603   \n\n       0_RMSSD (ms)  0_NNxx  0_pNNxx (%)  ...  11_Max_P  11_Min_P  11_Max_Q  \\\n0       1164.946351       7    77.777778  ...     0.125    -0.025     0.015   \n1        256.886958       8    66.666667  ...     0.090     0.005    -0.010   \n2         36.055513       2    20.000000  ...     0.160    -0.080    -0.060   \n3         31.819805       1    11.111111  ...     0.020    -0.020    -0.080   \n4         42.573466       2    22.222222  ...     0.060    -0.080    -0.040   \n...             ...     ...          ...  ...       ...       ...       ...   \n18806   1165.651259      11    73.333333  ...     0.185    -0.055    -0.030   \n18807   1362.071033       8    88.888889  ...     0.185     0.000     0.050   \n18808   3157.126858       5    71.428571  ...     0.075     0.000    -0.055   \n18809     49.470885       3    15.000000  ...     0.010    -0.050    -0.010   \n18810      9.074852       0     0.000000  ...     0.050    -0.090    -0.040   \n\n       11_Min_Q  11_Max_S  11_Min_S  11_Max_T  11_Min_T  diagnostic  \\\n0        -0.045    -0.300    -0.385     0.125    -0.070       CRBBB   \n1        -0.105    -0.320    -0.415     1.350    -0.040       CRBBB   \n2        -0.220    -0.160    -0.320     0.280    -0.060       CRBBB   \n3        -0.120    -0.180    -0.200     0.180    -0.040       CRBBB   \n4        -0.120    -0.160    -0.320     0.260     0.060       CRBBB   \n...         ...       ...       ...       ...       ...         ...   \n18806    -0.100    -0.615    -0.855     0.125     0.030        2AVB   \n18807    -0.030     0.015    -0.180     0.480    -0.045        2AVB   \n18808    -0.075     0.010    -0.045     0.190     0.015        2AVB   \n18809    -0.105    -0.275    -0.535     0.030     0.030        2AVB   \n18810    -0.120    -0.030    -0.170     0.130    -0.035        2AVB   \n\n                                                  rhythm  \n0                                                   AFIB  \n1                                                  SARRH  \n2                                                     SR  \n3                                                     SR  \n4                                                     SR  \n...                                                  ...  \n18806                        irregular sinus tachycardia  \n18807  --- warning: data quality may affect interpret...  \n18808  --- warning: data quality may affect interpret...  \n18809             *** consider acute st elevation mi ***  \n18810             *** consider acute st elevation mi ***  \n\n[18811 rows x 458 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0_Mean RR (ms)</th>\n      <th>0_STD RR/SDNN (ms)</th>\n      <th>0_Mean HR (Kubios' style) (beats/min)</th>\n      <th>0_Mean HR (beats/min)</th>\n      <th>0_STD HR (beats/min)</th>\n      <th>0_Min HR (beats/min)</th>\n      <th>0_Max HR (beats/min)</th>\n      <th>0_RMSSD (ms)</th>\n      <th>0_NNxx</th>\n      <th>0_pNNxx (%)</th>\n      <th>...</th>\n      <th>11_Max_P</th>\n      <th>11_Min_P</th>\n      <th>11_Max_Q</th>\n      <th>11_Min_Q</th>\n      <th>11_Max_S</th>\n      <th>11_Min_S</th>\n      <th>11_Max_T</th>\n      <th>11_Min_T</th>\n      <th>diagnostic</th>\n      <th>rhythm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4407.777778</td>\n      <td>875.249524</td>\n      <td>13.612301</td>\n      <td>14.200225</td>\n      <td>2.994709</td>\n      <td>10.507881</td>\n      <td>19.292605</td>\n      <td>1164.946351</td>\n      <td>7</td>\n      <td>77.777778</td>\n      <td>...</td>\n      <td>0.125</td>\n      <td>-0.025</td>\n      <td>0.015</td>\n      <td>-0.045</td>\n      <td>-0.300</td>\n      <td>-0.385</td>\n      <td>0.125</td>\n      <td>-0.070</td>\n      <td>CRBBB</td>\n      <td>AFIB</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3547.500000</td>\n      <td>220.949655</td>\n      <td>16.913319</td>\n      <td>16.976444</td>\n      <td>1.015075</td>\n      <td>15.151515</td>\n      <td>18.292683</td>\n      <td>256.886958</td>\n      <td>8</td>\n      <td>66.666667</td>\n      <td>...</td>\n      <td>0.090</td>\n      <td>0.005</td>\n      <td>-0.010</td>\n      <td>-0.105</td>\n      <td>-0.320</td>\n      <td>-0.415</td>\n      <td>1.350</td>\n      <td>-0.040</td>\n      <td>CRBBB</td>\n      <td>SARRH</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3980.000000</td>\n      <td>34.641016</td>\n      <td>15.075377</td>\n      <td>15.076531</td>\n      <td>0.132545</td>\n      <td>14.962594</td>\n      <td>15.345269</td>\n      <td>36.055513</td>\n      <td>2</td>\n      <td>20.000000</td>\n      <td>...</td>\n      <td>0.160</td>\n      <td>-0.080</td>\n      <td>-0.060</td>\n      <td>-0.220</td>\n      <td>-0.160</td>\n      <td>-0.320</td>\n      <td>0.280</td>\n      <td>-0.060</td>\n      <td>CRBBB</td>\n      <td>SR</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4497.777778</td>\n      <td>29.355211</td>\n      <td>13.339921</td>\n      <td>13.340492</td>\n      <td>0.087428</td>\n      <td>13.245033</td>\n      <td>13.483146</td>\n      <td>31.819805</td>\n      <td>1</td>\n      <td>11.111111</td>\n      <td>...</td>\n      <td>0.020</td>\n      <td>-0.020</td>\n      <td>-0.080</td>\n      <td>-0.120</td>\n      <td>-0.180</td>\n      <td>-0.200</td>\n      <td>0.180</td>\n      <td>-0.040</td>\n      <td>CRBBB</td>\n      <td>SR</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4595.555556</td>\n      <td>24.545247</td>\n      <td>13.056093</td>\n      <td>13.056467</td>\n      <td>0.069966</td>\n      <td>12.958963</td>\n      <td>13.186813</td>\n      <td>42.573466</td>\n      <td>2</td>\n      <td>22.222222</td>\n      <td>...</td>\n      <td>0.060</td>\n      <td>-0.080</td>\n      <td>-0.040</td>\n      <td>-0.120</td>\n      <td>-0.160</td>\n      <td>-0.320</td>\n      <td>0.260</td>\n      <td>0.060</td>\n      <td>CRBBB</td>\n      <td>SR</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>18806</th>\n      <td>2828.000000</td>\n      <td>808.498196</td>\n      <td>21.216407</td>\n      <td>22.524990</td>\n      <td>4.600528</td>\n      <td>13.245033</td>\n      <td>26.315789</td>\n      <td>1165.651259</td>\n      <td>11</td>\n      <td>73.333333</td>\n      <td>...</td>\n      <td>0.185</td>\n      <td>-0.055</td>\n      <td>-0.030</td>\n      <td>-0.100</td>\n      <td>-0.615</td>\n      <td>-0.855</td>\n      <td>0.125</td>\n      <td>0.030</td>\n      <td>2AVB</td>\n      <td>irregular sinus tachycardia</td>\n    </tr>\n    <tr>\n      <th>18807</th>\n      <td>4086.666667</td>\n      <td>749.192158</td>\n      <td>14.681892</td>\n      <td>15.221731</td>\n      <td>2.972090</td>\n      <td>12.096774</td>\n      <td>20.689655</td>\n      <td>1362.071033</td>\n      <td>8</td>\n      <td>88.888889</td>\n      <td>...</td>\n      <td>0.185</td>\n      <td>0.000</td>\n      <td>0.050</td>\n      <td>-0.030</td>\n      <td>0.015</td>\n      <td>-0.180</td>\n      <td>0.480</td>\n      <td>-0.045</td>\n      <td>2AVB</td>\n      <td>--- warning: data quality may affect interpret...</td>\n    </tr>\n    <tr>\n      <th>18808</th>\n      <td>5265.714286</td>\n      <td>2308.467996</td>\n      <td>11.394466</td>\n      <td>13.884246</td>\n      <td>5.884549</td>\n      <td>7.050529</td>\n      <td>21.428571</td>\n      <td>3157.126858</td>\n      <td>5</td>\n      <td>71.428571</td>\n      <td>...</td>\n      <td>0.075</td>\n      <td>0.000</td>\n      <td>-0.055</td>\n      <td>-0.075</td>\n      <td>0.010</td>\n      <td>-0.045</td>\n      <td>0.190</td>\n      <td>0.015</td>\n      <td>2AVB</td>\n      <td>--- warning: data quality may affect interpret...</td>\n    </tr>\n    <tr>\n      <th>18809</th>\n      <td>2202.000000</td>\n      <td>34.438351</td>\n      <td>27.247956</td>\n      <td>27.254547</td>\n      <td>0.421615</td>\n      <td>25.974026</td>\n      <td>28.037383</td>\n      <td>49.470885</td>\n      <td>3</td>\n      <td>15.000000</td>\n      <td>...</td>\n      <td>0.010</td>\n      <td>-0.050</td>\n      <td>-0.010</td>\n      <td>-0.105</td>\n      <td>-0.275</td>\n      <td>-0.535</td>\n      <td>0.030</td>\n      <td>0.030</td>\n      <td>2AVB</td>\n      <td>*** consider acute st elevation mi ***</td>\n    </tr>\n    <tr>\n      <th>18810</th>\n      <td>2396.111111</td>\n      <td>5.905637</td>\n      <td>25.040575</td>\n      <td>25.040727</td>\n      <td>0.061661</td>\n      <td>24.896266</td>\n      <td>25.104603</td>\n      <td>9.074852</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.050</td>\n      <td>-0.090</td>\n      <td>-0.040</td>\n      <td>-0.120</td>\n      <td>-0.030</td>\n      <td>-0.170</td>\n      <td>0.130</td>\n      <td>-0.035</td>\n      <td>2AVB</td>\n      <td>*** consider acute st elevation mi ***</td>\n    </tr>\n  </tbody>\n</table>\n<p>18811 rows × 458 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Обединение","metadata":{}},{"cell_type":"code","source":"ecg_df_HTV_mimic  =pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_mimic.csv\", index_col=0)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:26.241846Z","iopub.execute_input":"2024-06-17T16:52:26.242367Z","iopub.status.idle":"2024-06-17T16:52:29.555248Z","shell.execute_reply.started":"2024-06-17T16:52:26.242336Z","shell.execute_reply":"2024-06-17T16:52:29.554147Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"ecg_data_arrhytmia = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/df_arrhytmia.csv\", index_col=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:29.557351Z","iopub.execute_input":"2024-06-17T16:52:29.557757Z","iopub.status.idle":"2024-06-17T16:52:29.673378Z","shell.execute_reply.started":"2024-06-17T16:52:29.557720Z","shell.execute_reply":"2024-06-17T16:52:29.672212Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_ptb_xl = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV21728.csv\", index_col=0)\necg_data_ptb_xl = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/df_ptb_xl.csv\", index_col=0)\n\nfor i in ecg_data_ptb_xl.index:\n    if ecg_data_ptb_xl.electrodes_problems[i] in ecg_data_ptb_xl.electrodes_problems.value_counts().index:\n        ecg_data_ptb_xl = ecg_data_ptb_xl.drop(labels = [i],axis = 0)\necg_data_ptb_xl =ecg_data_ptb_xl[(ecg_data_ptb_xl.diagnostic != \"\") & (ecg_data_ptb_xl.rhythm != \"\")]\necg_df_HTV_ptb_xl[\"rhythm\"] = list(ecg_data_ptb_xl.rhythm)\necg_df_HTV_ptb_xl[\"diagnostic\"]=list(ecg_data_ptb_xl.diagnostic)\necg_df_HTV_ptb_xl[\"age\"]=list(ecg_data_ptb_xl.age)\necg_df_HTV_ptb_xl[\"sex\"]=list(ecg_data_ptb_xl.sex)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:29.674728Z","iopub.execute_input":"2024-06-17T16:52:29.675120Z","iopub.status.idle":"2024-06-17T16:52:51.116631Z","shell.execute_reply.started":"2024-06-17T16:52:29.675081Z","shell.execute_reply":"2024-06-17T16:52:51.115720Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"#ecg_data_arrhytmia\necg_df_arrhytmia = pd.read_csv(\"/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_HTV_arrhytmia8000.csv\", index_col=0)\n\necg_df_arrhytmia[\"rhythm\"] = list(ecg_data_arrhytmia.rhythm)[:len(ecg_df_arrhytmia)]\necg_df_arrhytmia[\"diagnostic\"]=list(ecg_data_arrhytmia.diagnostic)[:len(ecg_df_arrhytmia)]\necg_df_arrhytmia[\"age\"]=list(ecg_data_arrhytmia.Age)[:len(ecg_df_arrhytmia)]\necg_df_arrhytmia[\"sex\"]=list(ecg_data_arrhytmia.Sex)[:len(ecg_df_arrhytmia)]","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:51.119054Z","iopub.execute_input":"2024-06-17T16:52:51.119535Z","iopub.status.idle":"2024-06-17T16:52:52.537105Z","shell.execute_reply.started":"2024-06-17T16:52:51.119485Z","shell.execute_reply":"2024-06-17T16:52:52.536029Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"NEW_def = pd.concat([\n    ecg_df_HTV_mimic,\n    ecg_df_HTV_ptb_xl,\n    ecg_df_arrhytmia\n])","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.538416Z","iopub.execute_input":"2024-06-17T16:52:52.538815Z","iopub.status.idle":"2024-06-17T16:52:52.628824Z","shell.execute_reply.started":"2024-06-17T16:52:52.538778Z","shell.execute_reply":"2024-06-17T16:52:52.627628Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"NEW_def.index = pd.RangeIndex(start=0, stop=len(NEW_def) )","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.630398Z","iopub.execute_input":"2024-06-17T16:52:52.630742Z","iopub.status.idle":"2024-06-17T16:52:52.635729Z","shell.execute_reply.started":"2024-06-17T16:52:52.630711Z","shell.execute_reply":"2024-06-17T16:52:52.634586Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"NEW_def[\"sex\"] = NEW_def[\"sex\"].replace({\"Female\":1,\"Male\":0})","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.637013Z","iopub.execute_input":"2024-06-17T16:52:52.637370Z","iopub.status.idle":"2024-06-17T16:52:52.669073Z","shell.execute_reply.started":"2024-06-17T16:52:52.637337Z","shell.execute_reply":"2024-06-17T16:52:52.667977Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2201942663.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n  NEW_def[\"sex\"] = NEW_def[\"sex\"].replace({\"Female\":1,\"Male\":0})\n","output_type":"stream"}]},{"cell_type":"code","source":"path = '/kaggle/input/ptb-xl-a-large-publicly-available2/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/'\n\nscp_data = pd.read_csv(path + 'scp_statements.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.672452Z","iopub.execute_input":"2024-06-17T16:52:52.672878Z","iopub.status.idle":"2024-06-17T16:52:52.693748Z","shell.execute_reply.started":"2024-06-17T16:52:52.672839Z","shell.execute_reply":"2024-06-17T16:52:52.692793Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"def hampel(vals_orig):\n    vals = vals_orig.copy()    \n    difference = np.abs(vals.median()-vals)\n    median_abs_deviation = difference.median()\n    threshold = 3 * median_abs_deviation\n    outlier_idx = difference > threshold\n    vals[outlier_idx] = np.nan\n    return(vals)\n\nimport numpy as np\nfrom scipy import stats\n\ndef grubbs_stat(y):\n    std_dev = np.std(y)\n    avg_y = np.mean(y)\n    abs_val_minus_avg = abs(y - avg_y)\n    max_of_deviations = max(abs_val_minus_avg)\n    max_ind = np.argmax(abs_val_minus_avg)\n    Gcal = max_of_deviations / std_dev\n    print(f\"Grubbs Statistics Value: {Gcal}\")\n    return Gcal, max_ind\n\ndef calculate_critical_value(size, alpha):\n    t_dist = stats.t.ppf(1 - alpha / (2 * size), size - 2)\n    numerator = (size - 1) * np.sqrt(np.square(t_dist))\n    denominator = np.sqrt(size) * np.sqrt(size - 2 + np.square(t_dist))\n    critical_value = numerator / denominator\n    print(f\"Grubbs Critical Value: {critical_value}\")\n    return critical_value\n\ndef check_G_values(Gs, Gc, inp, max_index):\n    if Gs > Gc:\n        print(f\"{inp[max_index]} is an outlier\")\n    else:\n        print(f\"{inp[max_index]} is not an outlier\")","metadata":{"execution":{"iopub.status.busy":"2024-06-03T02:39:19.657505Z","iopub.status.idle":"2024-06-03T02:39:19.657825Z","shell.execute_reply.started":"2024-06-03T02:39:19.657665Z","shell.execute_reply":"2024-06-03T02:39:19.657680Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df =NEW_def.copy()\ndf_numeric = df.select_dtypes(include=[np.number])\nnumeric_cols = df_numeric.columns.values\nGcritical = calculate_critical_value(len(df[numeric_cols[0]]), 0.1)\nGstat, max_index = grubbs_stat(df[numeric_cols[0]])\ncheck_G_values(Gstat, Gcritical, df[numeric_cols[0]], max_index)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def Entering_missing_values(df,name):\n    df_numeric = df.select_dtypes(include=[np.number])\n    numeric_cols = df_numeric.columns.values\n    for i in df[name].value_counts().index:\n        df_1 = df[df[name] == i]\n        for col in numeric_cols:\n            if (np.mean(df_1[col].isnull()) > 0) and (np.mean(df_1[col].isnull()) < 1):\n                df_1[col] = df_1[col].fillna(df_1[col].median())                        \n        df[df[name] == i] = df_1\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.694999Z","iopub.execute_input":"2024-06-17T16:52:52.695394Z","iopub.status.idle":"2024-06-17T16:52:52.702329Z","shell.execute_reply.started":"2024-06-17T16:52:52.695359Z","shell.execute_reply":"2024-06-17T16:52:52.701247Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\npd.options.mode.chained_assignment = None  # default='warn'","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.703586Z","iopub.execute_input":"2024-06-17T16:52:52.703905Z","iopub.status.idle":"2024-06-17T16:52:52.711649Z","shell.execute_reply.started":"2024-06-17T16:52:52.703878Z","shell.execute_reply":"2024-06-17T16:52:52.710643Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"len(NEW_def)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.712758Z","iopub.execute_input":"2024-06-17T16:52:52.713036Z","iopub.status.idle":"2024-06-17T16:52:52.726282Z","shell.execute_reply.started":"2024-06-17T16:52:52.713012Z","shell.execute_reply":"2024-06-17T16:52:52.725235Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"48539"},"metadata":{}}]},{"cell_type":"code","source":"NEW_def_r = NEW_def[[\"age\",\"sex\",\"diagnostic\"]]\nNEW_def_d = NEW_def[[\"age\",\"sex\",\"rhythm\"]]","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.727593Z","iopub.execute_input":"2024-06-17T16:52:52.728004Z","iopub.status.idle":"2024-06-17T16:52:52.740967Z","shell.execute_reply.started":"2024-06-17T16:52:52.727973Z","shell.execute_reply":"2024-06-17T16:52:52.739934Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"NEW_def_r=Entering_missing_values(NEW_def_r,\"diagnostic\")\nNEW_def_d=Entering_missing_values(NEW_def_d,\"rhythm\")","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:52:52.742297Z","iopub.execute_input":"2024-06-17T16:52:52.742641Z","iopub.status.idle":"2024-06-17T16:53:06.090898Z","shell.execute_reply.started":"2024-06-17T16:52:52.742594Z","shell.execute_reply":"2024-06-17T16:53:06.089773Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"NEW_def.age = pd.DataFrame([NEW_def_r.age,NEW_def_d.age]).mean()\nNEW_def.sex = pd.DataFrame([NEW_def_r.sex,NEW_def_d.sex]).mean()","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:06.093670Z","iopub.execute_input":"2024-06-17T16:53:06.093999Z","iopub.status.idle":"2024-06-17T16:53:06.949491Z","shell.execute_reply.started":"2024-06-17T16:53:06.093971Z","shell.execute_reply":"2024-06-17T16:53:06.948248Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"NEW_def.age[NEW_def.age.isnull()] = NEW_def.age.mean()\nNEW_def.sex[NEW_def.sex.isnull()] = 0.5","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:06.950777Z","iopub.execute_input":"2024-06-17T16:53:06.951096Z","iopub.status.idle":"2024-06-17T16:53:06.961254Z","shell.execute_reply.started":"2024-06-17T16:53:06.951068Z","shell.execute_reply":"2024-06-17T16:53:06.960296Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2472953177.py:1: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf[\"col\"][row_indexer] = value\n\nUse `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n  NEW_def.age[NEW_def.age.isnull()] = NEW_def.age.mean()\n/tmp/ipykernel_33/2472953177.py:2: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf[\"col\"][row_indexer] = value\n\nUse `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n\n  NEW_def.sex[NEW_def.sex.isnull()] = 0.5\n","output_type":"stream"}]},{"cell_type":"code","source":"NEW_def.age = NEW_def.age.astype(int)\nNEW_def.sex = NEW_def.sex.astype(int)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:06.962663Z","iopub.execute_input":"2024-06-17T16:53:06.962986Z","iopub.status.idle":"2024-06-17T16:53:06.970595Z","shell.execute_reply.started":"2024-06-17T16:53:06.962959Z","shell.execute_reply":"2024-06-17T16:53:06.969565Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"df = NEW_def.copy()","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:06.972040Z","iopub.execute_input":"2024-06-17T16:53:06.972574Z","iopub.status.idle":"2024-06-17T16:53:07.249030Z","shell.execute_reply.started":"2024-06-17T16:53:06.972533Z","shell.execute_reply":"2024-06-17T16:53:07.248110Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# импорт пакетов\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport matplotlib\nplt.style.use('ggplot')\nfrom matplotlib.pyplot import figure\n\n%matplotlib inline\nmatplotlib.rcParams['figure.figsize'] = (12,8)\n\npd.options.mode.chained_assignment = None\n","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:07.250356Z","iopub.execute_input":"2024-06-17T16:53:07.250688Z","iopub.status.idle":"2024-06-17T16:53:07.839890Z","shell.execute_reply.started":"2024-06-17T16:53:07.250659Z","shell.execute_reply":"2024-06-17T16:53:07.838751Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"Пропуски - желтым","metadata":{}},{"cell_type":"code","source":"def f___(i):\n    cols = df.columns[38*i:38*(i+1)] # первые 30 колонок\n    colours = ['#000099', '#ffff00']\n    if (i)%2 == 0:\n      plt.figure(figsize=(30,10))\n    plt.subplot((12)//2 +1, 2, i+1)\n    sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colours))\n\nfor i in range(0,12):\n    f___(i)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:53:47.156756Z","iopub.execute_input":"2024-06-17T16:53:47.157119Z","iopub.status.idle":"2024-06-17T16:54:29.605687Z","shell.execute_reply.started":"2024-06-17T16:53:47.157092Z","shell.execute_reply":"2024-06-17T16:54:29.604383Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 3000x1000 with 4 Axes>","image/png":"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значения в строку","metadata":{}},{"cell_type":"code","source":"for col in NEW_def.columns:\n    pct_missing = np.mean(NEW_def[col].isnull())\n    if round(pct_missing*100) >1:\n        print('{} - {}%'.format(col, round(pct_missing*100)))","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:55:29.314290Z","iopub.execute_input":"2024-06-17T16:55:29.315246Z","iopub.status.idle":"2024-06-17T16:55:29.449323Z","shell.execute_reply.started":"2024-06-17T16:55:29.315200Z","shell.execute_reply":"2024-06-17T16:55:29.448062Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"3_Mean RR (ms) - 2%\n3_STD RR/SDNN (ms) - 2%\n3_Mean HR (Kubios' style) (beats/min) - 2%\n3_Mean HR (beats/min) - 2%\n3_STD HR (beats/min) - 2%\n3_Min HR (beats/min) - 2%\n3_Max HR (beats/min) - 2%\n3_RMSSD (ms) - 2%\n3_NNxx - 2%\n3_pNNxx (%) - 2%\n3_Max_R - 2%\n3_Min_R - 2%\n3_Max_RR - 2%\n3_Min_RR - 2%\n3_Max_P - 2%\n3_Min_P - 2%\n3_Max_Q - 2%\n3_Min_Q - 2%\n3_Max_S - 2%\n3_Min_S - 2%\n3_Max_T - 2%\n3_Min_T - 2%\n6_Mean RR (ms) - 2%\n6_STD RR/SDNN (ms) - 2%\n6_Mean HR (Kubios' style) (beats/min) - 2%\n6_Mean HR (beats/min) - 2%\n6_STD HR (beats/min) - 2%\n6_Min HR (beats/min) - 2%\n6_Max HR (beats/min) - 2%\n6_RMSSD (ms) - 2%\n6_NNxx - 2%\n6_pNNxx (%) - 2%\n6_Max_R - 2%\n6_Min_R - 2%\n6_Max_RR - 2%\n6_Min_RR - 2%\n6_Max_P - 2%\n6_Min_P - 2%\n6_Max_Q - 2%\n6_Min_Q - 2%\n6_Max_S - 2%\n6_Min_S - 2%\n6_Max_T - 2%\n6_Min_T - 2%\ndiagnostic - 21%\nrhythm - 3%\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Стандартизация","metadata":{}},{"cell_type":"code","source":"def stardant(X_columns):\n    #определяем смещение Центрируем данные относительно 0\n    fix_displacement_min = X_columns.describe()\n    for i in X_columns.describe().columns:   \n        X_columns[i] = X_columns[i] - fix_displacement_min[i][\"min\"]\n    fix_displacement = X_columns.describe()\n    #Стандартизация по z-оценке\n    for i in X_columns.describe().columns:   \n        X_columns[i] = (X_columns[i] - fix_displacement[i][\"mean\"])/(fix_displacement[i][\"std\"])\n    return X_columns ,fix_displacement_min,fix_displacement#","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:55:35.812219Z","iopub.execute_input":"2024-06-17T16:55:35.813192Z","iopub.status.idle":"2024-06-17T16:55:35.819538Z","shell.execute_reply.started":"2024-06-17T16:55:35.813151Z","shell.execute_reply":"2024-06-17T16:55:35.818329Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"X_columns = NEW_def.copy().drop(\"rhythm\", axis= 1).drop(\"diagnostic\", axis= 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:55:39.208793Z","iopub.execute_input":"2024-06-17T16:55:39.209703Z","iopub.status.idle":"2024-06-17T16:55:39.587258Z","shell.execute_reply.started":"2024-06-17T16:55:39.209664Z","shell.execute_reply":"2024-06-17T16:55:39.586166Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"X_columns ,fix_displacement_min,fix_displacement =  stardant(X_columns)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T16:55:46.400128Z","iopub.execute_input":"2024-06-17T16:55:46.401059Z","iopub.status.idle":"2024-06-17T16:55:54.773331Z","shell.execute_reply.started":"2024-06-17T16:55:46.401022Z","shell.execute_reply":"2024-06-17T16:55:54.772349Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"X_columns.describe().T","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:24:51.661084Z","iopub.execute_input":"2024-06-17T17:24:51.661626Z","iopub.status.idle":"2024-06-17T17:24:54.186931Z","shell.execute_reply.started":"2024-06-17T17:24:51.661582Z","shell.execute_reply":"2024-06-17T17:24:54.185729Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"                                         count          mean  std        min  \\\n0_Mean RR (ms)                         48539.0 -4.450133e-17  1.0  -4.319134   \n0_STD RR/SDNN (ms)                     48539.0 -6.323873e-17  1.0  -0.660915   \n0_Mean HR (Kubios' style) (beats/min)  48539.0  6.932838e-16  1.0  -3.836008   \n0_Mean HR (beats/min)                  48539.0 -3.255623e-16  1.0  -3.787769   \n0_STD HR (beats/min)                   48539.0 -5.152785e-17  1.0  -0.611777   \n...                                        ...           ...  ...        ...   \n11_Min_S                               48449.0  7.921872e-15  1.0 -57.373917   \n11_Max_T                               48447.0  5.631895e-16  1.0  -5.050330   \n11_Min_T                               48447.0  1.897685e-15  1.0 -35.137168   \nage                                    48539.0 -7.494960e-17  1.0  -2.654243   \nsex                                    48539.0 -5.621220e-17  1.0  -0.666883   \n\n                                            25%       50%       75%        max  \n0_Mean RR (ms)                        -0.711196 -0.010704  0.702657   7.349530  \n0_STD RR/SDNN (ms)                    -0.538741 -0.404601  0.023693  17.524739  \n0_Mean HR (Kubios' style) (beats/min) -0.723341 -0.207967  0.495542   5.839092  \n0_Mean HR (beats/min)                 -0.721360 -0.208710  0.497266   5.655176  \n0_STD HR (beats/min)                  -0.517588 -0.434901 -0.113160  10.698695  \n...                                         ...       ...       ...        ...  \n11_Min_S                              -0.191804  0.260687  0.521739   3.929925  \n11_Max_T                              -0.501811 -0.194676  0.185586  54.957946  \n11_Min_T                              -0.398277 -0.041322  0.510769  28.524622  \nage                                   -0.256093  0.049126  0.310743  10.339370  \nsex                                   -0.666883 -0.666883  1.499483   1.499483  \n\n[458 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0_Mean RR (ms)</th>\n      <td>48539.0</td>\n      <td>-4.450133e-17</td>\n      <td>1.0</td>\n      <td>-4.319134</td>\n      <td>-0.711196</td>\n      <td>-0.010704</td>\n      <td>0.702657</td>\n      <td>7.349530</td>\n    </tr>\n    <tr>\n      <th>0_STD RR/SDNN (ms)</th>\n      <td>48539.0</td>\n      <td>-6.323873e-17</td>\n      <td>1.0</td>\n      <td>-0.660915</td>\n      <td>-0.538741</td>\n      <td>-0.404601</td>\n      <td>0.023693</td>\n      <td>17.524739</td>\n    </tr>\n    <tr>\n      <th>0_Mean HR (Kubios' style) (beats/min)</th>\n      <td>48539.0</td>\n      <td>6.932838e-16</td>\n      <td>1.0</td>\n      <td>-3.836008</td>\n      <td>-0.723341</td>\n      <td>-0.207967</td>\n      <td>0.495542</td>\n      <td>5.839092</td>\n    </tr>\n    <tr>\n      <th>0_Mean HR (beats/min)</th>\n      <td>48539.0</td>\n      <td>-3.255623e-16</td>\n      <td>1.0</td>\n      <td>-3.787769</td>\n      <td>-0.721360</td>\n      <td>-0.208710</td>\n      <td>0.497266</td>\n      <td>5.655176</td>\n    </tr>\n    <tr>\n      <th>0_STD HR (beats/min)</th>\n      <td>48539.0</td>\n      <td>-5.152785e-17</td>\n      <td>1.0</td>\n      <td>-0.611777</td>\n      <td>-0.517588</td>\n      <td>-0.434901</td>\n      <td>-0.113160</td>\n      <td>10.698695</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11_Min_S</th>\n      <td>48449.0</td>\n      <td>7.921872e-15</td>\n      <td>1.0</td>\n      <td>-57.373917</td>\n      <td>-0.191804</td>\n      <td>0.260687</td>\n      <td>0.521739</td>\n      <td>3.929925</td>\n    </tr>\n    <tr>\n      <th>11_Max_T</th>\n      <td>48447.0</td>\n      <td>5.631895e-16</td>\n      <td>1.0</td>\n      <td>-5.050330</td>\n      <td>-0.501811</td>\n      <td>-0.194676</td>\n      <td>0.185586</td>\n      <td>54.957946</td>\n    </tr>\n    <tr>\n      <th>11_Min_T</th>\n      <td>48447.0</td>\n      <td>1.897685e-15</td>\n      <td>1.0</td>\n      <td>-35.137168</td>\n      <td>-0.398277</td>\n      <td>-0.041322</td>\n      <td>0.510769</td>\n      <td>28.524622</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>48539.0</td>\n      <td>-7.494960e-17</td>\n      <td>1.0</td>\n      <td>-2.654243</td>\n      <td>-0.256093</td>\n      <td>0.049126</td>\n      <td>0.310743</td>\n      <td>10.339370</td>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <td>48539.0</td>\n      <td>-5.621220e-17</td>\n      <td>1.0</td>\n      <td>-0.666883</td>\n      <td>-0.666883</td>\n      <td>-0.666883</td>\n      <td>1.499483</td>\n      <td>1.499483</td>\n    </tr>\n  </tbody>\n</table>\n<p>458 rows × 8 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fix_displacement_min.to_csv(\"fix_displacement_min.csv\")\nfix_displacement.to_csv(\"fix_displacement.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:45:22.976012Z","iopub.execute_input":"2024-06-07T10:45:22.977018Z","iopub.status.idle":"2024-06-07T10:45:23.001569Z","shell.execute_reply.started":"2024-06-07T10:45:22.976977Z","shell.execute_reply":"2024-06-07T10:45:23.000445Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"X_columns[\"rhythm\"] = NEW_def[\"rhythm\"]\nX_columns[\"diagnostic\"] = NEW_def[\"diagnostic\"]","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:30.983358Z","iopub.execute_input":"2024-06-17T17:33:30.984104Z","iopub.status.idle":"2024-06-17T17:33:30.992459Z","shell.execute_reply.started":"2024-06-17T17:33:30.984067Z","shell.execute_reply":"2024-06-17T17:33:30.991143Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"NEW_def = X_columns","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:32.463494Z","iopub.execute_input":"2024-06-17T17:33:32.463887Z","iopub.status.idle":"2024-06-17T17:33:32.470602Z","shell.execute_reply.started":"2024-06-17T17:33:32.463856Z","shell.execute_reply":"2024-06-17T17:33:32.469365Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"NEW_def_d = NEW_def.copy().drop(\"rhythm\", axis= 1)\nNEW_def_r = NEW_def.copy().drop(\"diagnostic\", axis= 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:33.946867Z","iopub.execute_input":"2024-06-17T17:33:33.947926Z","iopub.status.idle":"2024-06-17T17:33:34.539910Z","shell.execute_reply.started":"2024-06-17T17:33:33.947875Z","shell.execute_reply":"2024-06-17T17:33:34.538962Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"NEW_def_d = NEW_def_d[(NEW_def_d.diagnostic.isnull()) != True]\nNEW_def_r = NEW_def_r[(NEW_def_r.rhythm.isnull()) != True]","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:35.242370Z","iopub.execute_input":"2024-06-17T17:33:35.242755Z","iopub.status.idle":"2024-06-17T17:33:35.390537Z","shell.execute_reply.started":"2024-06-17T17:33:35.242725Z","shell.execute_reply":"2024-06-17T17:33:35.389198Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"def sort_by_diagnostic(df,name):\n    list_diagnostic = []\n    for i in df[name].index:\n        k = df[name][i].split(\",\")\n        if len(k) > 1:\n            k.sort()\n            k = list(dict.fromkeys(k))\n        k = \",\".join(k)\n        list_diagnostic.append(k)\n    return  list_diagnostic","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:37.069424Z","iopub.execute_input":"2024-06-17T17:33:37.070908Z","iopub.status.idle":"2024-06-17T17:33:37.077294Z","shell.execute_reply.started":"2024-06-17T17:33:37.070857Z","shell.execute_reply":"2024-06-17T17:33:37.076047Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"NEW_def_d.diagnostic = sort_by_diagnostic(NEW_def_d,\"diagnostic\")\nNEW_def_r.rhythm = sort_by_diagnostic(NEW_def_r,\"rhythm\")","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:39.357089Z","iopub.execute_input":"2024-06-17T17:33:39.357489Z","iopub.status.idle":"2024-06-17T17:33:40.074784Z","shell.execute_reply.started":"2024-06-17T17:33:39.357447Z","shell.execute_reply":"2024-06-17T17:33:40.073277Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"#scp_data[\"Unnamed: 0\"]","metadata":{"execution":{"iopub.status.busy":"2024-06-02T15:14:14.921255Z","iopub.execute_input":"2024-06-02T15:14:14.921665Z","iopub.status.idle":"2024-06-02T15:14:14.926388Z","shell.execute_reply.started":"2024-06-02T15:14:14.921633Z","shell.execute_reply":"2024-06-02T15:14:14.925206Z"},"trusted":true},"execution_count":119,"outputs":[]},{"cell_type":"code","source":"NEW_def_r_1 = pd.DataFrame()\nfor i in scp_data[scp_data.rhythm == 1][\"Unnamed: 0\"]:\n    NEW_def_r_1 = pd.concat([\n        NEW_def_r_1,\n        NEW_def_r[NEW_def_r.rhythm == i]\n    ])","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:40.628479Z","iopub.execute_input":"2024-06-17T17:33:40.628878Z","iopub.status.idle":"2024-06-17T17:33:41.376652Z","shell.execute_reply.started":"2024-06-17T17:33:40.628839Z","shell.execute_reply":"2024-06-17T17:33:41.375697Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"NEW_def_r_1.rhythm.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:42.378154Z","iopub.execute_input":"2024-06-17T17:33:42.378558Z","iopub.status.idle":"2024-06-17T17:33:42.390536Z","shell.execute_reply.started":"2024-06-17T17:33:42.378526Z","shell.execute_reply":"2024-06-17T17:33:42.389431Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"rhythm\nSR       28278\nSBRAD     5692\nAFIB      4708\nSTACH     3742\nSARRH     1189\nAFLT       778\nSVTAC       39\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"NEW_def_d.diagnostic.value_counts()[:20]","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:44.162368Z","iopub.execute_input":"2024-06-17T17:33:44.162739Z","iopub.status.idle":"2024-06-17T17:33:44.175474Z","shell.execute_reply.started":"2024-06-17T17:33:44.162711Z","shell.execute_reply":"2024-06-17T17:33:44.174327Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"diagnostic\nNORM        9084\nLVH         2583\nLAFB        2525\nCLBBB       2357\n1AVB        2313\nCRBBB       2108\nLAO/LAE     2048\nNST_        2000\nNDT         1160\nIMI         1148\nRVH         1029\nIRBBB        934\nISCAL        601\nISC_,LVH     532\nASMI         441\nLPFB         419\nILBBB        413\nISCLA        327\nASMI,IMI     289\nRAO/RAE      270\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"NEW_def_r_1 = NEW_def_r_1.dropna()\nNEW_def_r_1.index = pd.RangeIndex(start=0, stop=len(NEW_def_r_1) )\nNEW_def_d = NEW_def_d.dropna()\nNEW_def_d.index = pd.RangeIndex(start=0, stop=len(NEW_def_d) )","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:33:48.193033Z","iopub.execute_input":"2024-06-17T17:33:48.193412Z","iopub.status.idle":"2024-06-17T17:33:48.356162Z","shell.execute_reply.started":"2024-06-17T17:33:48.193381Z","shell.execute_reply":"2024-06-17T17:33:48.355146Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"for col in NEW_def_r_1.columns:\n    pct_missing = np.mean(NEW_def_r_1[col].isnull())\n    if round(pct_missing*100) >0:\n        print('{} - {}%'.format(col, round(pct_missing*100)))","metadata":{"execution":{"iopub.status.busy":"2024-06-17T17:53:46.502911Z","iopub.execute_input":"2024-06-17T17:53:46.503283Z","iopub.status.idle":"2024-06-17T17:53:46.603226Z","shell.execute_reply.started":"2024-06-17T17:53:46.503237Z","shell.execute_reply":"2024-06-17T17:53:46.602175Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"NEW_def_r_1.to_csv(\"ecg_df_rhythm.csv\")\nNEW_def_d.to_csv(\"ecg_df_diagnostic.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-06-07T10:47:38.637666Z","iopub.execute_input":"2024-06-07T10:47:38.638090Z","iopub.status.idle":"2024-06-07T10:48:43.469857Z","shell.execute_reply.started":"2024-06-07T10:47:38.638056Z","shell.execute_reply":"2024-06-07T10:48:43.468893Z"},"trusted":true},"execution_count":61,"outputs":[]}]}